LEADER 05557nam 22006015 450 001 9910437566703321 005 20200706005058.0 010 $a1-299-40748-X 010 $a3-7091-1346-6 024 7 $a10.1007/978-3-7091-1346-2 035 $a(CKB)2550000001018181 035 $a(EBL)1083123 035 $a(OCoLC)832286055 035 $a(SSID)ssj0000879098 035 $a(PQKBManifestationID)11479448 035 $a(PQKBTitleCode)TC0000879098 035 $a(PQKBWorkID)10850876 035 $a(PQKB)11350194 035 $a(DE-He213)978-3-7091-1346-2 035 $a(MiAaPQ)EBC1083123 035 $a(PPN)16914108X 035 $a(EXLCZ)992550000001018181 100 $a20130321d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe Influence of Technology on Social Network Analysis and Mining$b[electronic resource] /$fedited by Tansel Özyer, Jon Rokne, Gerhard Wagner, Arno H.P. Reuser 205 $a1st ed. 2013. 210 1$aVienna :$cSpringer Vienna :$cImprint: Springer,$d2013. 215 $a1 online resource (651 p.) 225 1 $aLecture Notes in Social Networks,$x2190-5428 300 $aDescription based upon print version of record. 311 $a3-7091-1705-4 311 $a3-7091-1345-8 320 $aIncludes bibliographical references. 327 $aEgoClustering: Overlapping Community Detection via Merged Friendship-Groups -- Optimization Techniques for Multiple Centrality Computations -- Application of social network metrics to a trust-aware collaborative model for generating personalized user recommendations -- TweCoM: topic and context mining from Twitter -- Pixel-Oriented Network Visualization. Static Visualization of Change in Social Networks -- Building Expert Recommenders from Email-Based Personal Social Networks -- A local structure-based method for nodes clustering. Application to a large mobile phone social network -- Virus Propagation Modeling in Facebook -- Comparing and visualizing the social spreading of products on a large social network -- Engagingness and Responsiveness Behavior Models on the Enron Email Network and its Application to Email Reply Order Prediction -- Efficient Extraction of High-Betweenness Vertices from Heterogeneous Networks -- Cross-Domain Analysis of the Blogosphere for Trend Prediction -- Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection -- Clustering Social Networks Using Distance-Preserving Subgraph -- Extraction of Spatio-Temporal Data for Social Networks -- Detecting Communities in Massive Networks Efficiently with Flexible Resolution -- Detecting Emergent Behavior in Social Network of Agents -- Factors enabling information propagation in a Social Network Site -- Learning from the Past: An Analysis of Person Name Corrections in the DBLP Collection and Social Network Properties of Affected Entities -- Towards Leader based Recommendations -- Enhancing Child Safety in MMOs -- An Adaptive Framework for Discovery and Mining of User Profiles from Social Web-Based Interest Communities -- DB2SNA: an All-in-one Tool for Extraction and Aggregation of underlying Social Networks from Relational Databases -- Extending Social Network Analysis with Discourse Analysis - Combining Relational with Interpretive Data -- How Latent Class Models Matter to Social Network Analysis and Mining: Exploring the Emergence of Community -- Integrating Online Social Network Analysis in Personalized Web Search -- Evolution of Online Forum Communities -- Movie Rating Prediction with Matrix Factorization Algorithm. 330 $aThe study of social networks was originated in social and business communities. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding General areas of interest to the book include information science and mathematics, communication studies, business and organizational studies, sociology, psychology, anthropology, applied linguistics, biology and medicine. 410 0$aLecture Notes in Social Networks,$x2190-5428 606 $aComputer science 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 615 0$aComputer science. 615 14$aComputer Science, general. 676 $a006.754 702 $aÖzyer$b Tansel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRokne$b Jon$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWagner$b Gerhard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aReuser$b Arno H.P$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910437566703321 996 $aThe Influence of Technology on Social Network Analysis and Mining$92503780 997 $aUNINA